30468 results about "Data analysis" patented technology

The present disclosure provides for supplying collaborative planning by a computerized framework manager. Steps include furnishing network connections and collecting supply and demand information from business entities. Such information is analyzed and used to facilitate planning among the business entites. The network provides data access for multiple data sources and the network is used to store capacity data. The computerized framework manager conducts reverse inventory management of the business entities through the collection of data; analysis of data is then used to predict future demand, and predicted future demand is used to forecast costs to manufacturers, from which suggestions regarding optimization is made.

Here, we introduce Z-webs, including Z-factors and Z-nodes, for the understanding of relationships between objects, subjects, abstract ideas, concepts, or the like, including face, car, images, people, emotions, mood, text, natural language, voice, music, video, locations, formulas, facts, historical data, landmarks, personalities, ownership, family, friends, love, happiness, social behavior, voting behavior, and the like, to be used for many applications in our life, including on the search engine, analytics, Big Data processing, natural language processing, economy forecasting, face recognition, dealing with reliability and certainty, medical diagnosis, pattern recognition, object recognition, biometrics, security analysis, risk analysis, fraud detection, satellite image analysis, machine generated data analysis, machine learning, training samples, extracting data or patterns (from the video, images, and the like), editing video or images, and the like. Z-factors include reliability factor, confidence factor, expertise factor, bias factor, and the like, which is associated with each Z-node in the Z-web.

Methods and devices are provided for detecting and/or preventing cheating in online gaming. Some implementations provide gaming information in formats that are difficult for a bot to interpret, but which are preferably easy for humans to interpret. Some implementations of the invention involve the tracking and analysis of players' gaming activities. Some implementations of the invention provide a multi-tier approach to data analysis. Some analyses may be performed, for example, by a centralized computing device such as a server, whereas other analyses may be performed by a host device. In some implementations, “challenge and response” measures will be employed. Some implementations of the invention involve detection and prevention of collaboration between players.

A method for the detection of analytes using resonant mass sensors or sensor arrays comprises frequency encoding each sensor element, acquiring a time-domain resonance signal from the sensor or sensor array as it is exposed to analyte, detecting change in the frequency or resonant properties of each sensor element using a Fourier transform or other spectral analysis method, and classifying, identifying, and/or quantifying analyte using an appropriate data analysis procedure. Frequency encoded sensors or sensor arrays comprise sensor elements with frequency domain resonance signals that can be uniquely identified under a defined range of operating conditions. Frequency encoding can be realized either by fabricating individual sensor elements with unique resonant frequencies or by tuning or modifying identical resonant devices to unique frequencies by adding or removing mass from individual sensor elements. The array of sensor elements comprises multiple resonant structures that may have identical or unique sensing layers. The sensing layers influence the sensor elements' response to analyte. Time-domain signal is acquired, typically in a single data acquisition channel, and typically using either (1) a pulsed excitation followed by acquisition of the free oscillatory decay of the entire array or (2) a rapid scan acquisition of signal from the entire array in a direct or heterodyne configuration. Spectrum analysis of the time domain data is typically accomplished with Fourier transform analysis. The methods and sensor arrays of the invention enable rapid and sensitive analyte detection, classification and/or identification of complex mixtures and unknown compounds, and quantification of known analytes, using sensor element design and signal detection hardware that are robust, simple and low cost.

A health diagnostic system capable for monitoring the health condition of computing device on a network is disclosed. According to one aspect of the present invention, a computing device is installed a module that can provoke the monitoring process that periodically samples values representing the health condition of the computing device. The sampled values are then sent back to the monitoring server for analysis. Meanwhile a monitoring server receives the sampled values and performs data analysis to determine if the computing device is in a good/poor condition based on historic data that are further used to predicate what the remaining time is for the computing device to actually break down so that necessary measures may be taken to prevent an actual breakdown.

The present invention provides systems and methods for obtaining information from a networked system utilizing a distributed web crawler. The distributed nature of clients of a server is leveraged to provide fast and accurate web crawling data. Information gathered by a server's web crawler is compared to data retrieved by clients of the server to update the crawler's data. In one instance of the present invention, data comparison is achieved by utilizing information disseminated via a search engine results page. In another instance of the present invention, data validation is accomplished by client dictionaries, emanating from a server, that summarize web crawler data. The present invention also facilitates data analysis by providing a means to resist spoofing of a web crawler to increase data accuracy.

This invention provides methods for non-invasively monitoring patients with congestive heart failure (CHF) and for reporting and/or warning of changes in the patients' status. The methods gather physiological data and combine the data into parameters by which the severity of CHF can be judged. Preferred parameters include periodic breathing and heart rate variability. This invention also provides systems for carrying out these methods which permit patients to engage in their normal daily activities. Preferably, physiological data analysis is performed by a portable electronic unit carried by the patients.

Various embodiments of a method, system and article of manufacture to discover relationships among a first set of elements and a second set of elements are provided. At least one metric algorithm is identified based on a metric selection parameter. A raw result is determined based on the at least one metric algorithm, a first specified structural description of the first set of elements and a second specified structural description of the second set of elements. The raw result comprises a plurality of relationship measurements and the raw result is ordered. In some embodiments, a balanced result is produced based on the raw result and a matching strategy algorithm. In other embodiments, the matching strategy algorithm is identified based on a matching strategy selection parameter.

Embodiments of the present invention are directed to a search engine system, method and apparatus comprising a search engine, a participant management system, at least one storage database, a listing database and at least one feedback mechanism. The search engine accepts input data and utilizes the input data to search for information related to the search terms. Upon retrieval of relevant information, the data analysis module organizes the data related to the search terms into a search results list. To determine the placement of listings upon a search request, the data analysis module determines the probability, for each relevant listing, that the user will ultimately complete the desired action if the listing is shown, wherein the probability of action is determined by a plurality of factors. This probability is then combined with the specific CPA for the advertiser. Transaction information derived from a search and click is returned through the feedback mechanism to the search engine for revised calculation of listing placement.

The invention discloses a threat early warning and monitoring system and method based on big data analysis and a deployment architecture. The system comprises a data acquisition system module, which is used for carrying out real-time data acquisition on original network traffic; a data storage system module, which is used for carrying out data merging and data cleaning on the data collected by the data acquisition system module, and then, carrying out storage management; a real-time threat intelligent analysis system module, which is used for carrying out deep analysis and mining on security data through data mining, text analysis, traffic analysis, full-text search engine and real-time processing, and identifying unknown security threats in real time by combining an intrusion detection module, a network abnormal behavior module and a device abnormal behavior module; and a situation awareness display system module, which is used for carrying out comprehensive display on security threat situations stereoscopically in real time through a data visualization tool library. The threat early warning and monitoring system and method based on big data analysis and the deployment architecture are used for network security threat situation awareness and deep analysis under a plurality of service scenarios, and realize comprehensive abilities from attack early warning, attack identification to analysis and evidence obtaining.

Systems and methods that provide for automated research into the workings of one or more studied systems include automated research software modules that communicate with domain knowledge bases, research professionals, automated laboratories experiment objects, and data analysis processes, wherein automatically selected experiment objects can be run at an automated laboratory to produce experimental results, and the subsequent data-processing providing automated guidance to a next round of experiment choice and automated research. An Experiment Director rules engine chooses Experiment Objects based on user input through a Query Manager.

A system and a method of long-term condition monitoring of structures are based on use of autonomous sensing modules, centers for storing and processing data and software for data analysis. An autonomous sensing module contains a set of sensors for measurements of parameters related to the condition of a monitored structure, a non-volatile memory, a wireless data transfer unit, a controller, a clock circuit, a battery, an energy harvesting device and a power management unit. The autonomous sensing module provides a very long-term (40 years or more) functionality and reliability due to both use of at least near hermetic packages for the controller, the non-volatile memory, the battery, the clock circuit and the power management unit and choosing the duration of periods when the sensing module works in active mode in such a way that the average energy consumed by the autonomous sensing module is fully compensated by the average energy harvested by the energy harvesting device.

The present invention discloses various embodiments of multiple domain anomaly detection systems and methods. In one embodiment of the invention, a multiple domain anomaly detection system uses a generic learning procedure per domain to create a “normal data profile” for each domain based on observation of data per domain, wherein the normal data profile for each domain can be used to determine and compute domain-specific anomaly data per domain. Then, domain-specific anomaly data per domain can be analyzed together in a cross-domain fusion data analysis using one or more fusion rules. The fusion rules may involve comparison of domain-specific anomaly data from multiple domains to derive a multiple-domain anomaly score meter for a particular cross-domain analysis task. The multiple domain anomaly detection system and its related method may also utilize domain-specific anomaly indicators of each domain to derive a cross-domain anomaly indicator using the fusion rules.

Methods, devices and a system for disease management are provided that employ diagnostic testing devices (e.g., blood glucose meters) and medication delivery devices (e.g., insulin delivery devices) for providing data to a repository in real-time and automatically. Repository data can be analyzed to determine such information as actual test strip use, patient health parameters to outside prescribed ranges, testing and medication delivery compliance, patient profiles or stakeholders to receive promotional items or incentives, and so on. Connected meters and medication delivery devices and repository data analysis are also employed to associate a diagnostic test to a mealtime based on timing of a therapeutic intervention performed by an individual.

The invention discloses an information pushing system of a WiFi (wireless fidelity) terminal and an implementation method thereof. The system comprises a WiFi signal collector, a WiFi signal processing module, a WiFi indoor positioning module, a customer activity analyzing and data mining module, a user personality file database, an advertisement information storage database, an advertisement putting and matching engine and a WiFi logging portal module. The system is suitable for all terminals with the WiFi function, such as mobile phones, PADs (personal access device) and PCs (personal computer), and different software platforms, such as Andriod, wP, iOS, Windows, MAC OS and Linux, the WiFi terminals do not need any software modification and updating, and the applicable range is wide. The possible hobby of a consumer is judged through data analysis and mining, and the real-time and on-site precise marketing is realized for target users, so on one hand, the experience of the user consuming in business places is improved, and on the other hand, the commercial value of sellers and factories is improved.

A distributed pattern recognition training method includes providing data communication between at least one central pattern analysis node and a plurality of peripheral data analysis sites. The method also includes communicating from the at least one central pattern analysis node to the plurality of peripheral data analysis a plurality of kernel-based pattern elements. The method further includes performing a plurality of iterations of pattern template training at each of the plurality of peripheral data analysis sites.

The invention discloses a system used for constructing a user figure. The system comprises a data acquisition module used for acquiring the Internet surfing log data of a user, a data pre-processing module used for pre-processing the Internet surfing log data of the user, a data analysis module used for carrying out characteristic extraction for the Internet surfing log data after pre-processing to acquire attribute characteristics of the user, a data application module used for matching in a multi-dimensional database on the basis of label classification trained by an established multi-dimensional characteristic database according to the attribute characteristics of the user to acquire multi-dimensional attribute labels of the user, and a data business module used for constructing the user figure according to the multi-dimensional attribute labels. The system is advantaged in that the holographic multi-dimensional user figure can be constructed, and rapid precise advertisement delivery of service providers/enterprises/companies and consumption behavior recommendation of use groups can be satisfied.